Update app.py

app.py

@@ -60,15 +60,17 @@ with gr.Blocks() as demo:
         # evaluate the model on the test set
         model.fit(x_train, y_train, epochs=5)
         test_loss, test_acc = model.evaluate(x_test,  y_test, verbose=2)
+        post_train_results = f"Test accuracy: {test_acc} Test Loss: {test_loss}"
+        print(post_train_results)
 
-        print("Test accuracy: ", test_acc)
-
-        # Define any necessary preprocessing steps for the image input here
-        # the image can be passed as a PIL or numpy
         # create the final model for production
         probability_model = tf.keras.Sequential([model, tf.keras.layers.Softmax()])
 
-        # Normalize the pixel values
+        # Input image pre-processing before submission to the model
+        # the image can be passed as a PIL or numpy
+
+        # Normalize the pixel values?
+        print(f"Input image shape: {img.shape}  Dimensions: {img.ndim} Array Element: {img[0]} ***********************************************************************")
         # assuming image_array is your input image array of shape (552, 3)
         resized_array = np.resize(img, (28, 28)) # resize the array to (28, 28)
         input_array = np.expand_dims(resized_array, axis=0) # add an extra dimension to represent the batch size
@@ -85,7 +87,6 @@ with gr.Blocks() as demo:
         with gr.Column(scale=2):
             image_data = gr.Image(label="Upload Image", type="numpy")
         with gr.Column(scale=1):
-            model_performance = gr.Text(label="Model Performance", interactive=False)
             model_prediction = gr.Text(label="Model Prediction", interactive=False)
     image_data.change(modelTraining, image_data, model_prediction)